CAREER: Scalable Remote Sensing Computational Framework for Near-real-time Crop Characterization

职业：用于近实时作物表征的可扩展遥感计算框架

• 批准号: 2048068
• 负责人: Chunyuan Diao
• 金额: $ 50.97万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2048068&HistoricalAwards=false
• 关键词: CAREER; Scalable; Remote; Sensing; Computational

The increasing proliferation of earth observation satellites, along with the explosive growth of remote sensing data, has dramatically facilitated timely land surface characterization worldwide. With several national and international agricultural initiatives, near-real-time crop type characterization has become vital for providing early warnings on food insecurity and timely crop yield forecasting, and for global food market transparency. However, near-real-time crop type characterization remains a challenge in agricultural remote sensing, due to the difficulty in collecting timely crop ground reference data, the limited generalizability of existing characterization models, and the lack of appropriate remote sensing cyberinfrastructure. Recent advances in satellite remote sensing and computational cyberinfrastructure open a new avenue to tackle the challenge. The overarching goal of the project is to establish a scalable remote sensing computational framework for near-real-time crop type characterization and to promote computational remote sensing education. The computational framework can transform the large-scale agricultural monitoring paradigm to meet the timely crop characterization requirements of global agricultural initiatives. The framework can substantially boost the ability to respond rapidly to emerging food crises, as well as create cross-cutting impacts in advancing a broad spectrum of remote sensing and agricultural research. The synergistic education and outreach activities offer unique learning opportunities about computational remote sensing to students from K-12 to the graduate level, and will broaden the participation of underrepresented students in computing. These activities also facilitate the open development and adoption of the computational framework across a range of disciplines. Therefore, this research aligns with the NSF mission to promote the progress of science and to advance the national health, prosperity, and welfare.The advanced remote sensing computational framework focuses on the development of a benchmark data repository called CropSight, a crop characterization modeling system, and a cutting-edge remote sensing cyberinfrastructure, to catalyze near-real-time crop and land surface characterizations. CropSight is a unique national-scale crop ground reference data repository, and embodies a wealth of season-long remotely sensed crop growth and environmental attributes across crop growing locations for most crop types in the U.S. CropSight can be generalized to continental and global scales, and will be used as a large-scale, systematic, and consistent ground reference data repository. The crop characterization system comprises a suite of novel deep learning-based computational models that can fuse the imagery from a set of earth observation satellites for timely crop monitoring, as well as identify varying crop types via innovative modeling of complex crop-environment interactions. The system will increase modeling generalizability for crop type characterization, and holds considerable potential to be extrapolated over wide geographical regions. The remote sensing cyberinfrastructure will include a highly scalable and cloud native implementation of the CropSight and a near-real-time on-demand crop monitoring system. With a serverless architecture, the project will build the cloud middleware that integrates various geospatial data sources and enables data-intensive remote sensing data analytics for timely crop characterization. The cyberinfrastructure will empower the paradigm shift from conventional compute-limited remote sensing analysis to planetary-scale massive imagery analysis for timely land surface monitoring.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

地球观测卫星的日益增加，沿着遥感数据的爆炸性增长，极大地促进了全世界及时的地表特征描述。随着一些国家和国际农业举措的实施，近实时作物类型特征描述对于提供粮食不安全预警和及时作物产量预测以及全球粮食市场透明度至关重要。然而，由于难以及时收集作物地面参考数据，现有表征模型的普遍性有限，以及缺乏适当的遥感网络基础设施，近实时作物类型表征仍然是农业遥感的一个挑战。卫星遥感和计算网络基础设施的最新进展为应对这一挑战开辟了新的途径。该项目的总体目标是建立一个可扩展的遥感计算框架，用于近实时作物类型定性，并促进计算遥感教育。计算框架可以改变大规模农业监测模式，以满足全球农业倡议的及时作物表征要求。该框架可大大提高迅速应对新出现的粮食危机的能力，并在推动广泛的遥感和农业研究方面产生跨领域的影响。协同教育和推广活动为从幼儿园到12年级直至研究生一级的学生提供了关于计算遥感的独特学习机会，并将扩大代表性不足的学生对计算的参与。这些活动还促进了计算框架在一系列学科中的开放开发和采用。因此，本研究符合NSF的使命，以促进科学的进步，促进国家的健康，繁荣和福利。先进的遥感计算框架的重点是开发一个基准数据库CropSight，作物表征建模系统，和一个尖端的遥感网络基础设施，以促进近实时的作物和地表特征。CropSight是一个独特的国家级农作物地面参考数据库，体现了丰富的季节长的遥感作物生长和环境属性跨作物生长地点的大多数作物类型在美国CropSight可以推广到大陆和全球范围内，并将被用作一个大规模的，系统的，和一致的地面参考数据库。作物表征系统包括一套基于深度学习的新型计算模型，可以融合来自一组地球观测卫星的图像，以及时监测作物，并通过对复杂作物与环境相互作用的创新建模来识别不同的作物类型。该系统将提高作物类型表征的建模通用性，并在广泛的地理区域外推具有相当大的潜力。遥感网络基础设施将包括CropSight的高度可扩展和云原生实施以及近实时按需作物监测系统。通过无服务器架构，该项目将构建云中间件，集成各种地理空间数据源，并实现数据密集型遥感数据分析，以及时进行作物表征。该网络基础设施将授权从传统的计算有限的遥感分析到行星尺度的大规模图像分析的范式转变，以及时进行地表监测。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology

• DOI: 10.3390/rs14091957
• 发表时间: 2022-04
• 期刊: Remote. Sens.
• 作者: C. Diao;Geyang Li

A Phenology-guided Bayesian-CNN (PB-CNN) framework for soybean yield estimation and uncertainty analysis

• DOI: 10.1016/j.isprsjprs.2023.09.025
• 发表时间: 2023-11
• 期刊: ISPRS Journal of Photogrammetry and Remote Sensing
• 影响因子: 12.7
• 作者: Chishan Zhang;C. Diao

CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation

• DOI: 10.1016/j.isprsjprs.2023.06.012
• 发表时间: 2023-08
• 期刊: ISPRS Journal of Photogrammetry and Remote Sensing
• 影响因子: 12.7
• 作者: Yin Liu;C. Diao;Zi-Ling Yang

Towards Scalable Within-Season Crop Mapping With Phenology Normalization and Deep Learning

• DOI: 10.1109/jstars.2023.3237500
• 发表时间: 2023
• 期刊: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• 影响因子: 5.5
• 作者: Zi-Ling Yang;C. Diao;F. Gao